def test_count_2gram(self):
model = AddOneNGram(2, self.sents)
counts = {
('<s>', ): 2,
('el', ): 1,
('gato', ): 1,
('come', ): 2,
('pescado', ): 1,
('.', ): 2,
('la', ): 1,
('gata', ): 1,
('salmón', ): 1,
('<s>', 'el'): 1,
('el', 'gato'): 1,
('gato', 'come'): 1,
('come', 'pescado'): 1,
('pescado', '.'): 1,
('.', '</s>'): 2,
('<s>', 'la'): 1,
('la', 'gata'): 1,
('gata', 'come'): 1,
('come', 'salmón'): 1,
('salmón', '.'): 1,
}
for gram, c in counts.items():
self.assertEqual(model.count(gram), c, gram)
# size of the vocabulary
self.assertEqual(model.V(), 9)
def test_count_2gram(self):
model = AddOneNGram(2, self.sents)
counts = {
('<s>',): 2,
('el',): 1,
('gato',): 1,
('come',): 2,
('pescado',): 1,
('.',): 2,
('la',): 1,
('gata',): 1,
('salmón',): 1,
('<s>', 'el'): 1,
('el', 'gato'): 1,
('gato', 'come'): 1,
('come', 'pescado'): 1,
('pescado', '.'): 1,
('.', '</s>'): 2,
('<s>', 'la'): 1,
('la', 'gata'): 1,
('gata', 'come'): 1,
('come', 'salmón'): 1,
('salmón', '.'): 1,
}
for gram, c in counts.items():
self.assertEqual(model.count(gram), c, gram)
# size of the vocabulary
self.assertEqual(model.V(), 9)
def test_norm_1gram(self):
model = AddOneNGram(1, self.sents)
tokens = {'el', 'gato', 'come', 'pescado', '.', 'la', 'gata', 'salmón', '</s>'}
prob_sum = sum(model.cond_prob(token) for token in tokens)
# prob_sum < 1.0 or almost equal to 1.0:
self.assertTrue(prob_sum < 1.0 or abs(prob_sum - 1.0) < 1e-10)
def test_count_3_addone_ngram(self):
sents_group = [self.sents2, self.sents]
i = 0
for sents_chosen in sents_group:
model = AddOneNGram(3, sents_chosen)
# sent2 vocab size = 8 and sent vocab size = 9
# (this explains use of i)
self.assertEqual(model.V(), 8+i)
i += 1
def test_norm_2gram(self):
model = AddOneNGram(2, self.sents)
tokens = {'el', 'gato', 'come', 'pescado', '.', 'la', 'gata', 'salmón', '</s>'}
for prev in list(tokens) + ['<s>']:
prob_sum = sum(model.cond_prob(token, [prev]) for token in tokens)
# prob_sum < 1.0 or almost equal to 1.0:
self.assertTrue(prob_sum < 1.0 or abs(prob_sum - 1.0) < 1e-10)
def test_cond_prob_4_addone_gram(self):
model = AddOneNGram(4, self.sents)
probs = {
('pescado', ('el', 'gato', 'come')): (1.0 + 1.0) / (1.0 + 9.0),
('salmón', ('la', 'gata', 'come')): (1.0 + 1.0) / (1.0 + 9.0),
('salame', ('el', 'gato', 'come')): 1.0 / (1.0 + 9.0),
}
for (token, prev), p in probs.items():
self.assertEqual(model.cond_prob(token, list(prev)), p)
def test_cond_prob_2gram(self):
model = AddOneNGram(2, self.sents)
probs = {
('pescado', 'come'): (1.0 + 1.0) / (2.0 + 9.0),
('salmón', 'come'): (1.0 + 1.0) / (2.0 + 9.0),
('salame', 'come'): 1.0 / (2.0 + 9.0),
}
for (token, prev), p in probs.items():
self.assertAlmostEqual(model.cond_prob(token, (prev, )), p)
def test_cond_prob_1gram(self):
model = AddOneNGram(1, self.sents)
probs = {
'pescado': (1.0 + 1.0) / (self.total + 9.0),
'come': (2.0 + 1.0) / (self.total + 9.0),
'salame': 1.0 / (self.total + 9.0),
}
for token, p in probs.items():
self.assertAlmostEqual(model.cond_prob(token), p)
def test_cond_prob_2gram(self):
model = AddOneNGram(2, self.sents)
probs = {
('pescado', 'come'): (1.0 + 1.0) / (2.0 + 9.0),
('salmón', 'come'): (1.0 + 1.0) / (2.0 + 9.0),
('salame', 'come'): 1.0 / (2.0 + 9.0),
}
for (token, prev), p in probs.items():
self.assertEqual(model.cond_prob(token, [prev]), p)
def test_cond_prob_1gram(self):
model = AddOneNGram(1, self.sents)
probs = {
'pescado': (1.0 + 1.0) / (12.0 + 9.0),
'come': (2.0 + 1.0) / (12.0 + 9.0),
'salame': 1.0 / (12.0 + 9.0),
}
for token, p in probs.items():
self.assertEqual(model.cond_prob(token), p)
def test_norm_3gram(self):
model = AddOneNGram(3, self.sents)
tokens = {'el', 'gato', 'come', 'pescado', '.', 'la', 'gata', 'salmón', '</s>'}
prevs = [['<s>', '<s>']] + \
[['<s>', t] for t in tokens] + \
[[t1, t2] for t1 in tokens for t2 in tokens]
for prev in prevs:
prob_sum = sum(model.cond_prob(token, prev) for token in tokens)
# prob_sum < 1.0 or almost equal to 1.0:
self.assertTrue(prob_sum < 1.0 or abs(prob_sum - 1.0) < 1e-10)
def test_get_most_probable_token_2gram(self):
model = AddOneNGram(2, self.sents)
ss = SentSorter(model)
prev_tokens = [('<s>', ), ('el', ), ('la', ), ('gato', ), ('gata', )]
possible_tokens = [['gato', 'come', 'el'], ['gata', 'gato'],
['gato', 'salta', 'gata'],
['salta', 'gata', 'come'], ['la', 'el', 'salta']]
for p_tokens, prev in zip(possible_tokens, prev_tokens):
self.assertEqual(ss.get_most_probable_token(p_tokens, prev_tokens),
tok[-1])
def test_sort_probable_sents_one_trainig_sent(self):
sents = self.sents[:1]
for i in range(2, 4):
model = AddOneNGram(i, sents)
unordered_sents = map(str.split, [
'el come gato', 'el gato come', 'gato come el', 'gato el come',
'come el gato', 'come gato el'
])
ss = SentSorter(model)
sorted_sents = ss.sort_probable_sents(unordered_sents)
for sent in sorted_sents:
self.assertEqual(sent, sents[0])
| \$?\d+(?:\.\d+)?%? # currency and percentages, e.g. $12.40, 82%
| \.\.\. # ellipsis
| [][.,;"'?():-_`] # these are separate tokens; includes ], [
'''
tokenizer = RegexpTokenizer(pattern)
root = '.'
corpus = PlaintextCorpusReader(root, 'books\.txt', word_tokenizer=tokenizer)
sents = corpus.sents()
# train the model
n = int(opts['-n'])
if opts['-m'] == 'addone':
model = AddOneNGram(n, sents)
elif opts['-m'] == 'inter':
gamma = opts['-g']
if gamma is None:
model = InterpolatedNGram(n, sents, None, False)
else:
model = InterpolatedNGram(n, sents, gamma, False)
elif opts['-m'] == 'interaddone':
gamma = opts['-g']
if gamma is None:
model = InterpolatedNGram(n, sents, None, True)
else:
model = InterpolatedNGram(n, sents, gamma, True)
else:
model = NGram(n, sents)